Spatiotemporal Analysis Techniques Research Articles

Spatiotemporal analysis of equine infectious anemia and prediction of risk areas in Europe

Equine Infectious Anemia (EIA) is a vector-borne persistent viral infection in equine animals. The EIA is characterized by recurrent fever, thrombocytopenia, depression, anemia, rapid weight loss, and lower body edema. Control of EIA is achieved through the elimination or isolation of infected animals, resulting in significant economic losses. In recent years, many countries in Europe have experienced outbreaks of EIA, which could potentially develop into a new wave of epidemic and pose a significant threat to the healthy development of the equine industry. This study utilized spatiotemporal analysis techniques and ecological niche modeling to investigate the spatiotemporal distribution characteristics of historical EIA outbreaks and predict risk areas for EIA occurrence in Europe. Spatiotemporal analysis results indicate that from 2005 to 2023, the EIA outbreaks in Europe exhibit five significant spatiotemporal clusters, with hotspots concentrated in southeastern France and northwestern Italy. Ecological niche modeling reveals that western, central, and southern Europe are high-risk areas for EIA outbreaks. Annual mean temperature, annual precipitation, and horse density are important variables that influence the occurrence of EIA. The results of this study can provide decision-makers with valuable insights, helping with EIA monitoring and resource allocation.

Assessment of vegetation net primary productivity variation and influencing factors in the Beijing-Tianjin-Hebei region

Exploring the spatiotemporal variations of vegetation net primary productivity (NPP) and analyzing the relationships between NPP and its influencing factors are vital for ecological protection in the Beijing-Tianjin-Hebei (BTH) region. In this study, we employed the CASA model in conjunction with spatiotemporal analysis techniques to estimate and analyze the spatiotemporal variations of NPP in BTH and different ecological function sub-regions over the past two decades. Subsequently, we established three scenarios (actual, climate-driven and land cover-driven) to assess the influencing factors and quantify their relative contributions. The results indicated that the overall NPP in BTH exhibited a discernible upward trend from 2000 to 2020, with a growth rate of 3.83 gC·m−2a−1. Furthermore, all six sub-regions exhibited an increase. The Bashang Plateau Ecological Protection Zone (BP) exhibited the highest growth rate (5.03 gC·m−2a−1), while the Low Plains Ecological Restoration Zone (LP) exhibited the lowest (2.07 gC·m−2a−1). Geographically, the stability of NPP exhibited a spatial pattern of gradual increase from west to east. Climate and land cover changes collectively increased NPP by 0.04 TgC·a−1 and 0.07 TgC·a−1, respectively, in the BTH region. Climate factors were found to have the greatest influence on NPP variations, contributing 40.49% across the BTH region. This influence exhibited a decreasing trend from northwest to southeast, with precipitation identified as the most influential climatic factor compared to temperature and solar radiation. Land cover change has profound effects on ecosystems, which is an important factor on NPP. From 2000 to 2020, 15.45% area of the BTH region underwent land cover type change, resulting in a total increase in NPP of 1.33 TgC. The conversion of grass into forest brought about the 0.89 TgC increase in NPP, which is the largest of all change types. In the area where land cover had undergone change, the land cover factor has been found to be the dominant factor influencing variations in NPP, with an average contribution of 49.37%. In contrast, in the south-central area where there has been no change in land cover, the residual factor has been identified as the most influential factor influencing variations in NPP. Our study highlights the important role of land cover change in influencing NPP variations in BTH. It also offers a novel approach to elucidating the influences of diverse factors on NPP, which is crucial for the scientific assessment of vegetation productivity and carbon sequestration capacity.

SPATIO-TEMPORAL DYNAMICS OF VISCERAL LEISHMANIASIS TRANSMISSION IN A HIGHLY ENDEMIC REGION OF BRAZIL

Background: Brazil concentrates 96% of visceral leishmaniasis cases occurred in Americas, and Maranhão is the Brazilian state with the highest number of registered cases. We aimed to analyze the spatio-temporal dynamics of visceral leishmaniasis transmission in a Brazilian region with high endemicity between 2009 to 2017. Methods: We carried out an ecological and time series study using spatio-temporal analysis techniques. A time-trend analysis was performed by the segmented linear regression. High-risk clusters were identified through spatial autocorrelation (Moran’s global and local indexes) and space-time scan statistics. Results: There were 5,128 visceral leishmaniasis cases, and it was observed increasing trends of new cases in the general population, among men, adults ≥20 years old, especially ≥60 years old. VL-HIV coinfection, mortality and lethality were crescent. Spatial analysis revealed intense visceral leishmaniasis transmission in almost the entire state and high-risk priority areas. Implications: Visceral leishmaniasis is in a substantial geographical expansion, demonstrating that the surveillance policies need to be reformulated.

A computational topology-based spatiotemporal analysis technique for honeybee aggregation

A primary challenge in understanding collective behavior is characterizing the spatiotemporal dynamics of the group. We employ topological data analysis to explore the structure of honeybee aggregations that form during trophallaxis, which is the direct exchange of food among nestmates. From the positions of individual bees, we build topological summaries called CROCKER matrices to track the morphology of the group as a function of scale and time. Each column of a CROCKER matrix records the number of topological features, such as the number of components or holes, that exist in the data for a range of analysis scales, at a given point in time. To detect important changes in the morphology of the group from this information, we first apply dimensionality reduction techniques to these matrices and then use classic clustering and change-point detection algorithms on the resulting scalar data. A test of this methodology on synthetic data from an agent-based model of honeybees and their trophallaxis behavior shows two distinct phases: a dispersed phase that occurs before food is introduced, followed by a food-exchange phase during which aggregations form. We then move to laboratory data, successfully detecting the same two phases across multiple experiments. Interestingly, our method reveals an additional phase change towards the end of the experiments, suggesting the possibility of another dispersed phase that follows the food-exchange phase.

Integrating Multiscale Geospatial Analysis for Monitoring Crop Growth, Nutrient Distribution, and Hydrological Dynamics in Large-Scale Agricultural Systems

Monitoring crop growth, soil conditions, and hydrological dynamics are imperative for sustainable agriculture and reduced environmental impacts. This interdisciplinary study integrates remote sensing, digital soil mapping, and hydrological data to elucidate intricate connections between these factors in the state of Ohio, USA. Advanced spatiotemporal analysis techniques were applied to key datasets, including the MODIS sensor satellite imagery, USDA crop data, soil datasets, Aster GDEM, and USGS stream gauge measurements. Vegetation indices derived from MODIS characterized crop-specific phenology and productivity patterns. Exploratory spatial data analysis show relationships of vegetation dynamics and soil properties, uncovering links between plant vigor, edaphic fertility, and nutrient distributions. Correlation analysis quantified these relationships and their seasonal evolution. Examination of stream gauge data revealed insights into spatiotemporal relationships of nutrient pollution and stream discharge. By synthesizing diverse geospatial data through cutting-edge data analytics, this work illuminated complex interactions between crop health, soil nutrients, and water quality in Ohio. The methodology and findings provide actionable perspectives to inform sustainable agricultural management and environmental policy. This study demonstrates the significant potential of open geospatial resources when integrated using a robust spatiotemporal framework. Integrating additional measurements and high-resolution data sources through advanced analytics and interactive visualizations could strengthen these insights.

A Multi-Framework of Google Earth Engine and GEV for Spatial Analysis of Extremes in Non-Stationary Condition in Southeast Queensland, Australia

The frequency and severity of extremes, including extreme precipitation events, extreme evapotranspiration and extreme water storage deficit events, are changing. Thus, the necessity for developing a framework that estimates non-stationary conditions is urgent. The aim of this paper is to develop a framework using the geeSEBAL platform, Generalised Extreme Value (GEV) models and spatiotemporal analysis techniques that incorporate the physical system in terms of cause and effect. Firstly, the geeSEBAL platform has enabled the estimation of actual evapotranspiration (ETa) with an unprecedented level of spatial-temporal resolution. Following this, the Non-stationary Extreme Value Analysis (NEVA) approach employs the Bayesian method using a Differential Evolution Markov Chain technique to calculate the frequency and magnitude of extreme values across the parameter space. Station and global climate datasets have been used to analyse the spatial and temporal variation of rainfall, reference evapotranspiration (ETo), ETa and water storage (WS) variables in the Lockyer Valley located in Southeast Queensland (SEQ), Australia. Frequency analysis of rainfall, ETa, and water storage deficit for 14 stations were performed using a GEV distribution under stationary and non-stationary assumptions. Comparing the ETa, ETo and ERA5 rainfall with station data showed reasonable agreement as follows: Pearson correlation of 0.59–0.75 for ETa, RMSE of 45.23–58.56 mm for ETa, Pearson correlation of 0.96–0.97 for ETo, RMSE of 73.13–87.73 mm for ETo and Pearson correlation of 0.87–0.92 for rainfall and RMSE of 37.53–57.10 mm for rainfall. The lower and upper uncertainty bounds between stationary and non-stationary conditions for rainfall station data of Gatton varied from 550.98 mm (stationary) to 624.97 mm (non-stationary), and for ERA5 rainfall datasets, 441.30 mm (stationary) to 450.77 mm (non-stationary). The results demonstrate that global climate datasets underestimate the difference between stationary and non-stationary conditions by 9.47 mm compared to results of 73.99 mm derived from station data. Similarly, the results demonstrate less variation between stationary and non-stationary conditions in water storage, followed by a sharp variation in rainfall and moderate variation in evapotranspiration. The findings of this study indicate that neglecting the non-stationary condition in some hydrometeorological variables can lead to underestimating their amounts. This framework can be applied to any geographical area for estimating extreme conditions, providing valuable insights for infrastructure planning and design, risk assessment and disaster management.

Impact of the COVID-19 Pandemic on Opioid Overdose Deaths: a Spatiotemporal Analysis.

The effects of the opioid crisis have varied across diverse and socioeconomically defined urban communities, due in part to widening health disparities. The onset of the COVID-19 pandemic has coincided with a spike in drug overdose deaths in the USA. However, the extent to which the impact of the pandemic on overdose deaths has varied across different demographics in urban neighborhoods is unclear. We examine the influence of COVID-19 pandemic on opioid overdose deaths through spatiotemporal analysis techniques. Using Milwaukee County, Wisconsin as a study site, we used georeferenced opioid overdose data to examine the locational and demographic differences in overdose deaths over time (2017–2020). We find that the pandemic significantly increased the monthly overdose deaths. The worst effects were seen in the poor, urban neighborhoods, affecting Black and Hispanic communities. However, more affluent, suburban White communities also experienced a rise in overdose deaths. A better understanding of contributing factors is needed to guide interventions at the local, regional, and national scales.

Spatial Analysis of COVID-19 Vaccination: A Scoping Review.

Spatial analysis of COVID-19 vaccination research is increasing in recent literature due to the availability of COVID-19 vaccination data that usually contain location components. However, to our knowledge, no previous study has provided a comprehensive review of this research area. Therefore, in this scoping review, we examined the breadth of spatial and spatiotemporal vaccination studies to summarize previous findings, highlight research gaps, and provide guidelines for future research. We performed this review according to the five-stage methodological framework developed by Arksey and O’Malley. We screened all articles published in PubMed/MEDLINE, Scopus, and Web of Science databases, as of 21 September 2021, that had employed at least one form of spatial analysis of COVID-19 vaccination. In total, 36 articles met the inclusion criteria and were organized into four main themes: disease surveillance (n = 35); risk analysis (n = 14); health access (n = 16); and community health profiling (n = 2). Our findings suggested that most studies utilized preliminary spatial analysis techniques, such as disease mapping, which might not lead to robust inferences. Moreover, few studies addressed data quality, modifiable areal unit problems, and spatial dependence, highlighting the need for more sophisticated spatial and spatiotemporal analysis techniques.

Projected climate impacts of large artificial reservoir impoundment in Yalong River Basin of southwestern China

AbstractCascade reservoirs were designed for Yalong River Basin, which ranks the 3rd largest hydropower base in China. Reservoir impoundment has certain impacts on local climate, but few researches focus on this field. This paper integrates the spatiotemporal analysis techniques and geostatistical methods to identify the reservoir projected meteorological indices (MIs) variations as well as the impact scope under different circumstances. Results show: The reservoir projected variations of moisture indexes were much more significant than temperatures. Anthropogenic disturbance has led to dramatic decrease of relative humidity in the past decade, far beyond its periodic amplitude range. Moreover, dry valley faced more serious drought risk but reservoir impoundment of this region alleviated the local drought risk. The daily minimum temperature, relative to the maximum temperature, was more sensitive to catchment changes with an earlier appearance of temperature zone variation. Since impounding, the MIs internal relationships in non-dry valley varied significantly more than that in dry valley, with the positive correlation of 0.7 between the relative humidity and precipitation weakened greatly. The severe warming hotspots distributed in the upper reach and had the probability of 0.95- 1 to exceed 1.5℃ IPCC control target. This study provides references for the disturbance of reservoir impoundment to local climate at multiple temporal-spatial scales under different circumstances and contributes to the MIs variation pattern identification and quantitative risk assessment.

Plumelets: Dynamic Filamentary Structures in Solar Coronal Plumes

Abstract Solar coronal plumes long seemed to possess a simple geometry supporting spatially coherent, stable outflow without significant fine structure. Recent high-resolution observations have challenged this picture by revealing numerous transient, small-scale, collimated outflows (“jetlets”) at the base of plumes. The dynamic filamentary structure of solar plumes above these outflows, and its relationship with the overall plume structure, have remained largely unexplored. We analyzed the statistics of continuously observed fine structure inside a single representative bright plume within a mid-latitude coronal hole during 2016 July 2–3. By applying advanced edge-enhancement and spatiotemporal analysis techniques to extended series of high-resolution images from the Solar Dynamics Observatory's Atmospheric Imaging Assembly, we determined that the plume was composed of numerous time-evolving filamentary substructures, referred to as “plumelets” in this paper, that accounted for most of the plume emission. The number of simultaneously identifiable plumelets was positively correlated with plume brightness, peaked in the fully formed plume, and remained saturated thereafter. The plumelets had transverse widths of 10 Mm and intermittently supported upwardly propagating periodic disturbances with phase speeds of 190–260 km s−1 and longitudinal wavelengths of 55–65 Mm. The characteristic frequency (≈ 3.3 mHz) is commensurate with that of solar p-modes. Oscillations in neighboring plumelets are uncorrelated, indicating that the waves could be driven by p-mode flows at spatial scales smaller than the plumelet separation. Multiple independent sources of outflow within a single coronal plume should impart significant fine structure to the solar wind that may be detectable by Parker Solar Probe and Solar Orbiter.

RETRACTED: Spatiotemporal Analysis of Tourists and Residents in Shanghai Based on Location-Based Social Network’s Data from Weibo

The aim of this study is to analyze and compare the patterns of behavior of tourists and residents from Location-Based Social Network (LBSN) data in Shanghai, China using various spatiotemporal analysis techniques at different venue categories. The paper presents the applications of location-based social network’s data by exploring the patterns in check-ins over a period of six months. We acquired the geo-location information from one of the most famous Chinese microblogs called Sina-Weibo (Weibo). The extracted data is translated into the Geographical Information Systems (GIS) format, and compared with the help of temporal statistical analysis and kernel density estimation. The venue classification is done by using information regarding the nature of physical locations. The findings reveal that the spatial activities of tourists are more concentrated as compared to those of residents, particularly in downtown, while the residents also visited suburban areas and the temporal activities of tourists varied significantly while the residents’ activities showed relatively stable behavior. These results can be applied in destination management, urban planning, and smart city development.

COVID-19 mortality in an area of northeast Brazil: epidemiological characteristics and prospective spatiotemporal modelling.

This study aimed to analyse the spatial-temporal distribution of COVID-19 mortality in Sergipe, Northeast, Brazil. It was an ecological study utilising spatiotemporal analysis techniques that included all deaths confirmed by COVID-19 in Sergipe, from 2 April to 14 June 2020. Mortality rates were calculated per 100 000 inhabitants and the temporal trends were analysed using a segmented log-linear model. For spatial analysis, the Kernel estimator was used and the crude mortality rates were smoothed by the empirical Bayesian method. The space-time prospective scan statistics applied the Poisson's probability distribution model. There were 391 COVID-19 registered deaths, with the majority among ⩾60 years old (62%) and males (53%). The most prevalent comorbidities were hypertension (40%), diabetes (31%) and cardiovascular disease (15%). An increasing mortality trend across the state was observed, with a higher increase in the countryside. An active spatiotemporal cluster of mortality comprising the metropolitan area and neighbouring cities was identified. The trend of COVID-19 mortality in Sergipe was increasing and the spatial distribution of deaths was heterogeneous with progression towards the countryside. Therefore, the use of spatial analysis techniques may contribute to surveillance and control of COVID-19 pandemic.

A Dynamic Spatiotemporal Analysis Model for Traffic Incident Influence Prediction on Urban Road Networks

Traffic incidents have a broad negative impact on both traffic systems and the quality of social activities; thus, analyzing and predicting the influence of traffic incidents dynamically is necessary. However, the traditional geographic information system for transportation (GIS-T) mostly presents fundamental data and static analysis, and transportation models focus predominantly on some typical road structures. Therefore, it is important to integrate transportation models with the spatiotemporal analysis techniques of GIS to address the dynamic process of traffic incidents. This paper presents a dynamic spatiotemporal analysis model to predict the influence of traffic incidents with the assistance of a GIS database and road network data. The model leverages a physical traffic shockwave model, and different superposition situations of shockwaves are proposed for both straight roads and road networks. Two typical cases were selected to verify the proposed model and were tested with the car-following model and real-world monitoring data. The results showed that the proposed model could successfully predict traffic effects with over 60% accuracy in both cases, and required less computational resources than the car-following model. Compared to other methods, the proposed model required fewer dynamic parameters and could be implemented on a wider set of road hierarchies.

Interactive Spatiotemporal Analysis of Oil Spills Using Comap in North Dakota

The aim of the study is to analyze the oil spill pattern from various types of incidents and contaminants to determine the extent that incident data can be used as a baseline to prevent hazardous material releases and improve response activities at a state level. This study addresses the importance of collecting and sharing oil spill incidents as well as analytics using the data. Temporal, spatial and spatiotemporal analysis techniques are employed for the oil-spill related environmental incidents observed in the state of North Dakota, United States of America, from 2000 to 2014, as a result of the oil boom. Specifically, spatiotemporal methods are used to examine how the patterns of environmental incidents in North Dakota, which vary with the time of day, the day, the month, and the season. Results indicate that there were critical spatial and time variations in the distribution of environmental incidents. Application of spatiotemporal interaction visualization techniques, called comap has the potential to help planners and decision makers formulate policy to mitigate the risks associated with environmental incidents, improve safety, and allocate resources.

Automated spatio-temporal analysis techniques for game environment

This paper introduces spatio-temporal analysis techniques for games. Game analytics is emerging field in Business Insight (BI) area. The benefits of adopting game analytics technique in commercial game development can help decision making in game design and quality assurance which are not quantified yet. For last 10 years, researchers in game user research area have proposed the frontier of techniques in game analytic field. Among them, spatio-temporal analysis field is most important for understanding of users in game environments intuitively. In this paper, we summarize four key areas of spatio-temporal analysis: visualization techniques, trajectory analysis, in-house telemetry system, and web-based middlewares in detail.

Comparing low‐frequency and intermittent variability in comprehensive climate models through nonlinear Laplacian spectral analysis

Nonlinear Laplacian spectral analysis (NLSA) is a recently developed technique for spatiotemporal analysis of high‐dimensional data, which represents temporal patterns via natural orthonormal basis functions on the nonlinear data manifold. Through such basis functions, determined efficiently via graph‐theoretic algorithms, NLSA captures intermittency, rare events, and other nonlinear dynamical features which are not accessible through linear approaches (e.g., singular spectrum analysis (SSA)). Here, we apply NLSA to study North Pacific SST monthly data from the CCSM3 and ECHAM5/MPI‐OM models. Without performing spatial coarse graining (i.e., operating in ambient‐space dimensions up to 1.6 × 105after lagged embedding), or seasonal‐cycle subtraction, the method reveals families of periodic, low‐frequency, and intermittent spatiotemporal modes. The intermittent modes, which describe variability in the Western and Eastern boundary currents, as well as variability in the subtropical gyre with year‐to‐year reemergence, are not captured by SSA, yet are likely to have high significance in a predictive context and utility in cross‐model comparisons.

Computer-Aided Detection of Diagnostic and Therapeutic Operations in Colonoscopy Videos

Colonoscopy is an endoscopic technique that allows a physician to inspect the inside of the human colon and to perform--if deemed necessary-at the same time a number of diagnostic and therapeutic operations. In order to see the inside of the colon, a video signal of the internal mucosa of the colon is generated by a tiny video camera at the tip of the endoscope and displayed on a monitor for real-time analysis by the physician. We have captured and stored these videos in digital format and call these colonoscopy videos. Based on new algorithms for instrument detection and shot segmentation, we introduce new spatio-temporal analysis techniques to automatically identify an operation shot--a segment of visual data in a colonoscopy video that corresponds to a diagnostic or therapeutic operation. Our experiments on real colonoscopy videos demonstrate the effectiveness of the proposed approach. The proposed techniques and software are useful for 1) postprocedure review for causes of complications due to diagnostic or therapeutic operations; 2) establishment of an effective content-based retrieval system to facilitate endoscopic research and education; 3) development of a systematic approach to assess and improve the procedural skills of endoscopists.

Using GIS to create synthetic disease outbreaks

BackgroundThe ability to detect disease outbreaks in their early stages is a key component of efficient disease control and prevention. With the increased availability of electronic health-care data and spatio-temporal analysis techniques, there is great potential to develop algorithms to enable more effective disease surveillance. However, to ensure that the algorithms are effective they need to be evaluated. The objective of this research was to develop a transparent user-friendly method to simulate spatial-temporal disease outbreak data for outbreak detection algorithm evaluation.A state-transition model which simulates disease outbreaks in daily time steps using specified disease-specific parameters was developed to model the spread of infectious diseases transmitted by person-to-person contact. The software was developed using the MapBasic programming language for the MapInfo Professional geographic information system environment.ResultsThe simulation model developed is a generalised and flexible model which utilises the underlying distribution of the population and incorporates patterns of disease spread that can be customised to represent a range of infectious diseases and geographic locations. This model provides a means to explore the ability of outbreak detection algorithms to detect a variety of events across a large number of stochastic replications where the influence of uncertainty can be controlled. The software also allows historical data which is free from known outbreaks to be combined with simulated outbreak data to produce files for algorithm performance assessment.ConclusionThis simulation model provides a flexible method to generate data which may be useful for the evaluation and comparison of outbreak detection algorithm performance.

Toward a Differential Calculus for Temporal Map Analysis

Investigators in many fields are analyzing temporal change in spatial data. Such analyses are typically conducted by comparing the value of some metric (e. g., area, contagion, or diversity indices) measured at time T1 with the value of the same metric measured at time T2 . These comparisons typically include the use of simple interpolation models to estimate the value of the metric of interest at points in time between observations, followed by applications of differential calculus to investigate the rates at which the metric is changing. Unfortunately, these techniques treat the values of the metrics being analyzed as if they were observed values, when in fact the metrics are derived from more fundamental spatial data. The consequence of treating metrics as observed values is a significant reduction in the degrees of freedom in spatial change over time. This results in an oversimplified view of spatio-temporal change. A more accurate view can be produced by (1) applying temporal interpolation models to observed spatial data rather than derived spatial metrics; (2) expanding the metric of interest's computational equation by replacing the terms relating to the observed spatial data with their temporal interpolation equations; and (3) differentiating the expanded computational equation. This alternative, three-step spatio-temporal analysis technique will be described and justified. The alternative technique will be compared to the conventional approach using common metrics and a sample data set.

Unraveling the cerebral dynamics of mental imagery

Evidence from functional brain imaging studies suggests that mental imagery processes, like other higher cognitive functions, simultaneously activate different neuronal networks involving multiple cortical areas. The question of whether these different areas are truly simultaneously active or whether they are temporally distinct and might reflect different steps of information processing cannot be answered by these imaging methods. We applied spatiotemporal analysis techniques to multichannel event-related potential (ERP) recordings in order to elucidate the topography and chronology of brain processes involved in mental rotation. We measured 41-electrode ERPs in 12 healthy subjects who had to evaluate whether rotated letters were in a normal or mirror-reflected position. These figures were presented in the left, right, or central visual fields and were randomly rotated by 0 degrees, 50 degrees, 100 degrees, or 150 degrees. Behaviorally, we replicated the observation that reaction time increases with greater angles of rotation. Electrophysiologically, we identified a set of dominant electric potential distributions, each of them stable for a certain time period. Only one of these time segments (appearing between 400-600 msec) increased significantly in duration with greater angles of rotation mirroring reaction time. We suggest that the rotation of mental images is carried out during this time segment. A general linear inverse solution applied to this segment showed occipito-parietal cerebral activity that was lateralized to the right hemisphere.